What is the name of your organization?
Verto Health
What is the name of your solution?
Verto Connect
Provide a one-line summary or tagline for your solution.
Transforming Unstructured Data into Reliable, Actionable Intelligence for Advanced Healthcare
In what city, town, or region is your solution team headquartered?
Toronto, ON, Canada
In what country is your solution team headquartered?
Canada
What type of organization is your solution team?
For-profit, including B-Corp or similar models
Film your elevator pitch.
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What specific problem are you solving?
The majority of healthcare data (~80%) resides within unstructured clinical narratives (physician notes, reports) (Sedlakova, Jana et al., 2023). The urgency of rapid decision-making within Electronic Medical Records (EMRs) by frontline workers frequently results in the creation of semi-structured data, where entire data structures or form values are embedded within single fields. Extracting & mapping information from these free-text records to standardized formats demands significant clinical expertise & processing semi-structured data necessitates intricate, custom-built Extract, Transform, Load (ETL) pipelines. Current interoperability standards struggle to access this substantial data volume due to its deep integration within existing clinical workflows - a challenge anticipated to escalate with the growing adoption of AI-powered tools. Transforming this data into discrete, standardized values would unlock a crucial data domain currently inaccessible to interoperability efforts, significantly enhancing the potential for seamless information exchange and improved healthcare outcomes. The need & impact is seen in national initiatives (e.g., Canada Health Infoway, Sequoia Project) - even in digitally advanced health systems (e.g., South Korea), we have heard from leaders that unstructured data is an ongoing gap impacting clinical care, analytics, & research, adding multiple hours of manual work to highly skilled & resource constrained clinical resources to close gaps in care & research.
What is your solution?
Verto Connect employs AI-driven techniques to convert raw healthcare data into structured, coded information, enabling a wide range of downstream applications - from population health management to seamless interoperability & data exchange for individual patient records.
To integrate across disparate data systems, we use AI-powered schema matching, a scalable approach supported by cross-industry research that surpasses the limitations of traditional rule-based methods. To extract crucial information from clinical text, we automate the generation of standardized Fast Healthcare Interoperability Resources (FHIR) outputs, specifically addressing the reliability and scalability challenges inherent in current AI methodologies. These standard FHIR outputs represent data meticulously structured & formatted according to the FHIR standard, which defines a unified framework for exchanging healthcare information between diverse computer systems, irrespective of their underlying storage mechanisms.
To effectively understand, categorize, & map medical terminology, our technology leverages AI to automatically link data to Unified Medical Language System (UMLS), empowering users to explore related medical concepts across different systems. Through our Verto connect platform, we are enabling more informed decisions at both the health system & individual care provision levels.
Who does your solution serve, and in what ways will the solution impact their lives?
The Verto solution serves stakeholders across the healthcare system, including but not limited to:
-Clinicians: Coded unstructured data offers a more complete patient view, potentially improving decision-making and streamlining workflows by making information easier to access. The most immediate impact is typically seen in reducing the administrative burden on resource constrained clinicians to have to manually review coding, often involving time-consuming chart & data reviews.
-Clinical Coders: Automation of routine coding will shift their focus to complex cases and data validation, requiring new skills in working with AI systems. This can increase efficiency, scalability, and potentially lead to more meaningful work. Similarly to clinicians, the most immediate impact is reduced administrative burden & increased efficiency.
-Data Analysts: Unlocking unstructured data provides a comprehensive source for analysis, enabling the discovery of new insights and improving predictive modeling capabilities built off of a significantly larger dataset.
-Population Health Management Leaders: Access to coded unstructured data allows for a deeper understanding of population health, improved identification of risk factors, and the development of more targeted interventions. It also enhances monitoring, evaluation, and the ability to address health equity.
